Overview

Dataset statistics

Number of variables5
Number of observations29
Missing cells2
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory45.6 B

Variable types

Numeric1
Text4

Dataset

Description인천광역시 미추홀구 대리운전 업체에 대한 데이터이며 연번, 상호명, 도로명주소, 전화번호 등의 항목을 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15086998&srcSe=7661IVAWM27C61E190

Alerts

도로명주소 has 2 (6.9%) missing valuesMissing
연번 has unique valuesUnique
지번주소 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-01-28 13:05:16.644775
Analysis finished2024-01-28 13:05:17.044923
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T22:05:17.095714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q322
95-th percentile27.6
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.56764621
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)7
Skewness0
Sum435
Variance72.5
MonotonicityStrictly increasing
2024-01-28T22:05:17.201188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1
 
3.4%
2 1
 
3.4%
29 1
 
3.4%
28 1
 
3.4%
27 1
 
3.4%
26 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 1
 
3.4%
22 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1 1
3.4%
2 1
3.4%
3 1
3.4%
4 1
3.4%
5 1
3.4%
6 1
3.4%
7 1
3.4%
8 1
3.4%
9 1
3.4%
10 1
3.4%
ValueCountFrequency (%)
29 1
3.4%
28 1
3.4%
27 1
3.4%
26 1
3.4%
25 1
3.4%
24 1
3.4%
23 1
3.4%
22 1
3.4%
21 1
3.4%
20 1
3.4%

상호명
Text

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-01-28T22:05:17.382128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.1034483
Min length4

Characters and Unicode

Total characters206
Distinct characters76
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)82.8%

Sample

1st row미추홀구대리운전
2nd row인천대리운전
3rd row문학대리운전
4th row인천대리운전
5th row주안대리운전
ValueCountFrequency (%)
인천대리운전 3
 
9.7%
파이브대리운전 2
 
6.5%
미추홀구대리운전 1
 
3.2%
인천동부모범운전자회 1
 
3.2%
kh탁송대리운전 1
 
3.2%
에플대리운전 1
 
3.2%
용현점 1
 
3.2%
대리운전 1
 
3.2%
그린플래닛 1
 
3.2%
스타천지왓썹요대리운전 1
 
3.2%
Other values (18) 18
58.1%
2024-01-28T22:05:17.667060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 24
 
11.7%
전 23
 
11.2%
대 23
 
11.2%
운 23
 
11.2%
천 9
 
4.4%
인 6
 
2.9%
탁 5
 
2.4%
송 5
 
2.4%
이 4
 
1.9%
스 4
 
1.9%
Other values (66) 80
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 198
96.1%
Decimal Number 4
 
1.9%
Space Separator 2
 
1.0%
Lowercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 24
 
12.1%
전 23
 
11.6%
대 23
 
11.6%
운 23
 
11.6%
천 9
 
4.5%
인 6
 
3.0%
탁 5
 
2.5%
송 5
 
2.5%
이 4
 
2.0%
스 4
 
2.0%
Other values (60) 72
36.4%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
6 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 198
96.1%
Common 6
 
2.9%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 24
 
12.1%
전 23
 
11.6%
대 23
 
11.6%
운 23
 
11.6%
천 9
 
4.5%
인 6
 
3.0%
탁 5
 
2.5%
송 5
 
2.5%
이 4
 
2.0%
스 4
 
2.0%
Other values (60) 72
36.4%
Common
ValueCountFrequency (%)
0 2
33.3%
2
33.3%
2 1
16.7%
6 1
16.7%
Latin
ValueCountFrequency (%)
k 1
50.0%
h 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 198
96.1%
ASCII 8
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 24
 
12.1%
전 23
 
11.6%
대 23
 
11.6%
운 23
 
11.6%
천 9
 
4.5%
인 6
 
3.0%
탁 5
 
2.5%
송 5
 
2.5%
이 4
 
2.0%
스 4
 
2.0%
Other values (60) 72
36.4%
ASCII
ValueCountFrequency (%)
0 2
25.0%
2
25.0%
k 1
12.5%
h 1
12.5%
2 1
12.5%
6 1
12.5%

지번주소
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-01-28T22:05:17.843954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length21.310345
Min length14

Characters and Unicode

Total characters618
Distinct characters49
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 주안동 29-4
2nd row인천광역시 미추홀구 용현동 630-75
3rd row인천광역시 미추홀구 문학동 378-21
4th row인천광역시 미추홀구 숭의동 440-1
5th row인천광역시 미추홀구 주안동 106-1 6층
ValueCountFrequency (%)
인천광역시 29
23.6%
미추홀구 29
23.6%
주안동 7
 
5.7%
용현동 7
 
5.7%
숭의동 4
 
3.3%
도화동 4
 
3.3%
문학동 3
 
2.4%
관교동 2
 
1.6%
학익동 2
 
1.6%
47-3 1
 
0.8%
Other values (35) 35
28.5%
2024-01-28T22:05:18.117725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94
 
15.2%
동 30
 
4.9%
인 29
 
4.7%
광 29
 
4.7%
역 29
 
4.7%
시 29
 
4.7%
미 29
 
4.7%
추 29
 
4.7%
홀 29
 
4.7%
구 29
 
4.7%
Other values (39) 262
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
58.9%
Decimal Number 131
 
21.2%
Space Separator 94
 
15.2%
Dash Punctuation 26
 
4.2%
Uppercase Letter 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 30
8.2%
인 29
 
8.0%
광 29
 
8.0%
역 29
 
8.0%
시 29
 
8.0%
미 29
 
8.0%
추 29
 
8.0%
홀 29
 
8.0%
구 29
 
8.0%
천 29
 
8.0%
Other values (24) 73
20.1%
Decimal Number
ValueCountFrequency (%)
1 24
18.3%
3 15
11.5%
0 14
10.7%
4 14
10.7%
9 12
9.2%
8 11
8.4%
6 11
8.4%
5 10
7.6%
2 10
7.6%
7 10
7.6%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
I 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
58.9%
Common 251
40.6%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 30
8.2%
인 29
 
8.0%
광 29
 
8.0%
역 29
 
8.0%
시 29
 
8.0%
미 29
 
8.0%
추 29
 
8.0%
홀 29
 
8.0%
구 29
 
8.0%
천 29
 
8.0%
Other values (24) 73
20.1%
Common
ValueCountFrequency (%)
94
37.5%
- 26
 
10.4%
1 24
 
9.6%
3 15
 
6.0%
0 14
 
5.6%
4 14
 
5.6%
9 12
 
4.8%
8 11
 
4.4%
6 11
 
4.4%
5 10
 
4.0%
Other values (2) 20
 
8.0%
Latin
ValueCountFrequency (%)
L 1
33.3%
I 1
33.3%
S 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
58.9%
ASCII 254
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94
37.0%
- 26
 
10.2%
1 24
 
9.4%
3 15
 
5.9%
0 14
 
5.5%
4 14
 
5.5%
9 12
 
4.7%
8 11
 
4.3%
6 11
 
4.3%
5 10
 
3.9%
Other values (5) 23
 
9.1%
Hangul
ValueCountFrequency (%)
동 30
8.2%
인 29
 
8.0%
광 29
 
8.0%
역 29
 
8.0%
시 29
 
8.0%
미 29
 
8.0%
추 29
 
8.0%
홀 29
 
8.0%
구 29
 
8.0%
천 29
 
8.0%
Other values (24) 73
20.1%

도로명주소
Text

MISSING 

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Memory size364.0 B
2024-01-28T22:05:18.323331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length23.777778
Min length17

Characters and Unicode

Total characters642
Distinct characters76
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 염창로 46
2nd row인천광역시 미추홀구 아암대로107번길 37-18
3rd row인천광역시 미추홀구 매소홀로546번길 7-5
4th row인천광역시 미추홀구 주안중로 44-5 6층
5th row인천광역시 미추홀구 재넘이길9번길 35
ValueCountFrequency (%)
인천광역시 27
22.5%
미추홀구 27
22.5%
석정로 2
 
1.7%
15 2
 
1.7%
101동 1
 
0.8%
경인로268번길 1
 
0.8%
5-1 1
 
0.8%
인하로405번길 1
 
0.8%
매소홀로562번길 1
 
0.8%
9-33 1
 
0.8%
Other values (56) 56
46.7%
2024-01-28T22:05:18.643247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
14.5%
인 34
 
5.3%
홀 30
 
4.7%
시 28
 
4.4%
미 28
 
4.4%
광 27
 
4.2%
역 27
 
4.2%
추 27
 
4.2%
구 27
 
4.2%
천 27
 
4.2%
Other values (66) 294
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 400
62.3%
Decimal Number 131
 
20.4%
Space Separator 93
 
14.5%
Dash Punctuation 13
 
2.0%
Uppercase Letter 4
 
0.6%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 34
 
8.5%
홀 30
 
7.5%
시 28
 
7.0%
미 28
 
7.0%
광 27
 
6.8%
역 27
 
6.8%
추 27
 
6.8%
구 27
 
6.8%
천 27
 
6.8%
로 22
 
5.5%
Other values (49) 123
30.8%
Decimal Number
ValueCountFrequency (%)
1 27
20.6%
3 17
13.0%
5 15
11.5%
2 15
11.5%
4 13
9.9%
0 12
9.2%
6 11
8.4%
8 10
 
7.6%
9 6
 
4.6%
7 5
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
N 1
25.0%
S 1
25.0%
I 1
25.0%
L 1
25.0%
Space Separator
ValueCountFrequency (%)
93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 400
62.3%
Common 237
36.9%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 34
 
8.5%
홀 30
 
7.5%
시 28
 
7.0%
미 28
 
7.0%
광 27
 
6.8%
역 27
 
6.8%
추 27
 
6.8%
구 27
 
6.8%
천 27
 
6.8%
로 22
 
5.5%
Other values (49) 123
30.8%
Common
ValueCountFrequency (%)
93
39.2%
1 27
 
11.4%
3 17
 
7.2%
5 15
 
6.3%
2 15
 
6.3%
- 13
 
5.5%
4 13
 
5.5%
0 12
 
5.1%
6 11
 
4.6%
8 10
 
4.2%
Other values (2) 11
 
4.6%
Latin
ValueCountFrequency (%)
N 1
20.0%
S 1
20.0%
I 1
20.0%
L 1
20.0%
s 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 400
62.3%
ASCII 242
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93
38.4%
1 27
 
11.2%
3 17
 
7.0%
5 15
 
6.2%
2 15
 
6.2%
- 13
 
5.4%
4 13
 
5.4%
0 12
 
5.0%
6 11
 
4.5%
8 10
 
4.1%
Other values (7) 16
 
6.6%
Hangul
ValueCountFrequency (%)
인 34
 
8.5%
홀 30
 
7.5%
시 28
 
7.0%
미 28
 
7.0%
광 27
 
6.8%
역 27
 
6.8%
추 27
 
6.8%
구 27
 
6.8%
천 27
 
6.8%
로 22
 
5.5%
Other values (49) 123
30.8%

전화번호
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-01-28T22:05:18.822822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.103448
Min length9

Characters and Unicode

Total characters351
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row032-1666-1636
2nd row032-1533-6022
3rd row0507-976-7783
4th row080-870-1919
5th row070-4552-3339
ValueCountFrequency (%)
032-1666-1636 1
 
3.4%
032-710-0125 1
 
3.4%
032-872-1057 1
 
3.4%
1800-5131 1
 
3.4%
032-719-7415 1
 
3.4%
032-719-7301 1
 
3.4%
032-719-7312 1
 
3.4%
0507-1367-3159 1
 
3.4%
1666-8255 1
 
3.4%
032-433-2519 1
 
3.4%
Other values (19) 19
65.5%
2024-01-28T22:05:19.108028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54
15.4%
- 54
15.4%
3 45
12.8%
1 36
10.3%
7 34
9.7%
2 31
8.8%
5 24
6.8%
6 20
 
5.7%
4 19
 
5.4%
9 18
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297
84.6%
Dash Punctuation 54
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54
18.2%
3 45
15.2%
1 36
12.1%
7 34
11.4%
2 31
10.4%
5 24
8.1%
6 20
 
6.7%
4 19
 
6.4%
9 18
 
6.1%
8 16
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54
15.4%
- 54
15.4%
3 45
12.8%
1 36
10.3%
7 34
9.7%
2 31
8.8%
5 24
6.8%
6 20
 
5.7%
4 19
 
5.4%
9 18
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54
15.4%
- 54
15.4%
3 45
12.8%
1 36
10.3%
7 34
9.7%
2 31
8.8%
5 24
6.8%
6 20
 
5.7%
4 19
 
5.4%
9 18
 
5.1%

Interactions

2024-01-28T22:05:16.847157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T22:05:19.188238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명지번주소도로명주소전화번호
연번1.0000.9631.0001.0001.000
상호명0.9631.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000

Missing values

2024-01-28T22:05:16.945510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T22:05:17.017232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번상호명지번주소도로명주소전화번호
01미추홀구대리운전인천광역시 미추홀구 주안동 29-4인천광역시 미추홀구 염창로 46032-1666-1636
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